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- F. Boenisch, C Mühl, R. Rinberg, J. Ihrig, A. Dziedzic. Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. Accepted to PoPETs 2023.
- R. Rinberg, N. Agarwal: "Privacy when Everyone is Watching: An SOK on Anonymity on the Blockchain"., 2022
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation, 2022.
- A. Tamaskar, R. Rinberg, S. Chakraborty, B. Mishra: "Creolizing the Web". 2021.
- Pre-Print: Improvements and Analysis of Private Ensemble-Based Federated Learning (available upon request)
- Privacy When Everyone is Watching : Privacy on the Blockchain in the Presence of KYC laws
- Multi-party Computation (MPC) and Trustless Wage Sharing explainer
- I lead a group of cross-university graduate students who meet and discuss privacy and security from technical, legal, and policy perspectives. We are called Technically Private (though informally sometimes go by Privacy Peeps). Please reach out if you are interested in joining.
- Technically Private Substack
- Medium
- Scientific American : Jell-O Brains and DNA: High School Students Launch Innovative STEM Program
- Under the Tree. 2019. 2nd Assistant Camera. Website.